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Weather Regimes are large-scale, quasi-stationary and recurrent meteorological configurations. Their dynamics largely impact the main weather (temperature, precipitation, wind speed) and energy-related (power demand, hydro/wind/solar generation) variables.
In the context of the forecasts, weather regimes are generally more predictable than usual 15-day forecasts of singular weather quantities. Hence, quantifying the consequences these conformations have on energy variables entails an opportunity to detect trends with larger anticipation, and it can be a valuable information for the energy trading sector.
In our study, we thus give an overview of the physical nature of such regimes, presenting a set of advanced statistical methodologies in the Unsupervised Learning setting, in order to recognize and discern them, proving to outperform previous studies in literature. We leverage on such results to perform a fine-grained quantification on energy variables, envisioning pervasive applications in the trading of energy commodities. Finally, we provide a forecasting method projecting the results collected in the previous steps into a sub-seasonal window (until 6 weeks in advance), which we apply on the Winter 2021-2022.



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